Algorithm used to transform the data:
lars: uses the least angle regression method (linear_model.lars_path)
lasso_lars: uses Lars to compute the Lasso solution
lasso_cd: uses the coordinate descent method to compute the
Lasso solution (linear_model.Lasso). lasso_lars will be faster if
the estimated components are sparse.
omp: uses orthogonal matching pursuit to estimate the sparse solution
threshold: squashes to zero all coefficients less than alpha from
the projection dictionary*X'

transform_n_nonzero_coefs:int, 0.1*n_features by default

Number of nonzero coefficients to target in each column of the
solution. This is only used by algorithm=’lars’ and algorithm=’omp’
and is overridden by alpha in the omp case.

transform_alpha:float, 1. by default

If algorithm=’lasso_lars’ or algorithm=’lasso_cd’, alpha is the
penalty applied to the L1 norm.
If algorithm=’threshold’, alpha is the absolute value of the
threshold below which coefficients will be squashed to zero.
If algorithm=’omp’, alpha is the tolerance parameter: the value of
the reconstruction error targeted. In this case, it overrides
n_nonzero_coefs.

split_sign:bool, False by default

Whether to split the sparse feature vector into the concatenation of
its negative part and its positive part. This can improve the
performance of downstream classifiers.

The method works on simple estimators as well as on nested objects
(such as pipelines). The latter have parameters of the form
<component>__<parameter> so that it’s possible to update each
component of a nested object.